Artificial intelligence in cancer pathology: Challenge to meet increasing demands of precision medicine

Clinical efforts on precision medicine are driving the need for accurate diagnostic, new prognostic and novel drug predictive assays to inform patient selection and stratification for disease treatment. Accumulating evidence suggests that a combination of cancer pathology and artificial intelligence...

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Bibliographic Details
Published inInternational journal of oncology Vol. 63; no. 3; p. 1
Main Authors Lai, Boan, Fu, Jianjiang, Zhang, Qingxin, Deng, Nan, Jiang, Qingping, Peng, Juan
Format Journal Article
LanguageEnglish
Published Greece Spandidos Publications 01.09.2023
Spandidos Publications UK Ltd
Subjects
Artificial intelligence
Brain cancer
Cancer
Care and treatment
Medical diagnosis
Neural networks
Neurophysiology
Pathology
Patient education
Precision medicine
China
diagnosis
precision medicine
cancer pathology
prognosis
artificial intelligence
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Summary:Clinical efforts on precision medicine are driving the need for accurate diagnostic, new prognostic and novel drug predictive assays to inform patient selection and stratification for disease treatment. Accumulating evidence suggests that a combination of cancer pathology and artificial intelligence (AI) can meet this requirement. In the present review, the past, present and emerging integrations of AI into cancer pathology were comprehensively reviewed, which were divided into four main groups to highlight the roles of AI‑integrated cancer pathology in precision medicine. Furthermore, the unsolved problems and future challenges in AI‑integrated cancer pathology were also discussed. It was found that, although AI‑integrated cancer pathology could enable the amalgamation of complex morphological phenotypes with the multi‑omics datasets that drove precision medicine, synergies of cancer pathology with other medical tools could be more promising for the clinic when making an accurate and rapid decision in personalized treatments for patients. It was hypothesized by the authors that exploring the potential advantages of the multimodal integration of cancer pathology, imaging‑omics, protein‑omics and other‑omics, as well as clinical data to decide upon appropriate management and improve patient outcomes may be the most challenging issue of cancer precision medicine in the future.
ISSN:1019-6439
1791-2423
DOI:10.3892/ijo.2023.5555